




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase_		:					Any          =  16
UpperCAmelCase_		:					int          =  32




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 16 ):
				__magic_name__						:						List[str]										=AutoTokenizer.from_pretrained("""bert-base-cased""" )
				__magic_name__						:						str										=load_dataset("""glue"""						,  """mrpc""" )

				def tokenize_function(lowerCamelCase ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__						:						List[str]										=tokenizer(examples["""sentence1"""]						,  examples["""sentence2"""]						,  truncation=lowerCamelCase						,  max_length=lowerCamelCase )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__magic_name__						:						int										=datasets.map(
								    lowerCamelCase						,  batched=lowerCamelCase						,  remove_columns=["""idx""", """sentence1""", """sentence2"""]						,  )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__magic_name__						:						Any										=tokenized_datasets.rename_column("""label"""						,  """labels""" )

				def collate_fn(lowerCamelCase ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__magic_name__						:						List[Any]										=128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__magic_name__						:						List[str]										=16
								elif accelerator.mixed_precision != "no":
												__magic_name__						:						Tuple										=8
								else:
												__magic_name__						:						Dict										=None

								return tokenizer.pad(
								    lowerCamelCase						,  padding="""longest"""						,  max_length=lowerCamelCase						,  pad_to_multiple_of=lowerCamelCase						,  return_tensors="""pt"""						,  )

				# Instantiate dataloaders.
				__magic_name__						:						Tuple										=DataLoader(
				    tokenized_datasets["""train"""]						,  shuffle=lowerCamelCase						,  collate_fn=lowerCamelCase						,  batch_size=lowerCamelCase )
				__magic_name__						:						Dict										=DataLoader(
				    tokenized_datasets["""validation"""]						,  shuffle=lowerCamelCase						,  collate_fn=lowerCamelCase						,  batch_size=lowerCamelCase )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							UpperCAmelCase_		:					Tuple          =  mocked_dataloaders  # noqa: F811




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,  lowerCamelCase ) == "1":
								__magic_name__						:						Tuple										=2
				# New Code #
				__magic_name__						:						List[Any]										=int(args.gradient_accumulation_steps )
				__magic_name__						:						int										=int(args.local_sgd_steps )
				# Initialize accelerator
				__magic_name__						:						List[str]										=Accelerator(
				    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  gradient_accumulation_steps=lowerCamelCase )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__						:						List[str]										=config["""lr"""]
				__magic_name__						:						Dict										=int(config["""num_epochs"""] )
				__magic_name__						:						Optional[int]										=int(config["""seed"""] )
				__magic_name__						:						Tuple										=int(config["""batch_size"""] )

				__magic_name__						:						Dict										=evaluate.load("""glue"""						,  """mrpc""" )

				set_seed(lowerCamelCase )
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=get_dataloaders(lowerCamelCase						,  lowerCamelCase )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__						:						str										=AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""						,  return_dict=lowerCamelCase )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__magic_name__						:						List[str]										=model.to(accelerator.device )

				# Instantiate optimizer
				__magic_name__						:						Tuple										=AdamW(params=model.parameters()						,  lr=lowerCamelCase )

				# Instantiate scheduler
				__magic_name__						:						Dict										=get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase						,  num_warmup_steps=100						,  num_training_steps=(len(lowerCamelCase ) * num_epochs)						,  )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						List[Any]										=accelerator.prepare(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

				# Now we train the model
				for epoch in range(lowerCamelCase ):
								model.train()
								with LocalSGD(
								    accelerator=lowerCamelCase						,  model=lowerCamelCase						,  local_sgd_steps=lowerCamelCase						,  enabled=local_sgd_steps is not None ) as local_sgd:
												for step, batch in enumerate(lowerCamelCase ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device )
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(lowerCamelCase ):
																				__magic_name__						:						Tuple										=model(**lowerCamelCase )
																				__magic_name__						:						Optional[int]										=output.loss
																				accelerator.backward(lowerCamelCase )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(lowerCamelCase ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												with torch.no_grad():
																__magic_name__						:						int										=model(**lowerCamelCase )
												__magic_name__						:						int										=outputs.logits.argmax(dim=-1 )
												__magic_name__					,			__magic_name__						:						Optional[int]										=accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=lowerCamelCase						,  references=lowerCamelCase						,  )

								__magic_name__						:						Tuple										=metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"						,  lowerCamelCase )




def 		lowerCAmelCase_ (						):
				__magic_name__						:						List[str]										=argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""						,  type=lowerCamelCase						,  default=lowerCamelCase						,  choices=["""no""", """fp16""", """bf16""", """fp8"""]						,  help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""						,  )
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps"""						,  type=lowerCamelCase						,  default=1						,  help="""The number of minibatches to be ran before gradients are accumulated."""						,  )
				parser.add_argument(
				    """--local_sgd_steps"""						,  type=lowerCamelCase						,  default=8						,  help="""Number of local SGD steps or None to disable local SGD""" )
				parser.add_argument("""--cpu"""						,  action="""store_true"""						,  help="""If passed, will train on the CPU.""" )
				__magic_name__						:						Tuple										=parser.parse_args()
				__magic_name__						:						Any										={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase						,  lowerCamelCase )


if __name__ == "__main__":
							main()











'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



